Content
87%Reviews the quality of instructions and guidance provided to agents. Good implementation is clear, handles edge cases, and produces reliable results.
A well-structured, actionable skill body with clean progressive disclosure. The only gap is the absence of explicit validation/verification checkpoints for the batch ingest/update operations, which keeps workflow clarity at 2.
Suggestions
Add an explicit validation checkpoint after ingest (e.g. verify JSONL is parseable and record count matches screener candidates before proceeding to update).
Add a verify step after the update command (e.g. confirm matured record count and that outcomes.3d/outcomes.5d are populated before summarizing).
Include a brief 'if a horizon's prices are missing, record PENDING and retry later' feedback loop so failed updates are handled deterministically.
| Dimension | Reasoning | Score |
|---|---|---|
Conciseness | Lean body with no basic-concept padding; assumes Claude's competence and every section earns its place (commands, fields, interpretation rules are directly useful). | 3 / 3 |
Actionability | Provides fully executable copy-paste commands with real flags and paths (e.g. 'python3 skills/.../build_model_book.py ingest --screener-json ... --horizons 3,5') plus concrete field lists and tag thresholds. | 3 / 3 |
Workflow Clarity | The four-step workflow is clearly sequenced, but ingest/update/summarize are batch operations over a durable JSONL store with no explicit validation checkpoint (e.g. confirm record count or JSON validity before proceeding), which caps clarity at 2 per the batch-operations guideline. It is not a 1 because the sequence and per-step outputs are present; not a 3 because no validate/fix/retry feedback loop is stated. | 2 / 3 |
Progressive Disclosure | SKILL.md is a concise overview with a Resources section signaling three one-level-deep references (model_book_schema.md, outcome_tags.md, review_workflow.md), all of which exist and none of which nest further. | 3 / 3 |
Total | 11 / 12 Passed |